{"type":"ScholarlyArticle","title":"Recommender Systems for Democracy: Toward Adversarial Robustness in Voting Advice Applications","authors":[{"name":"Frédéric Berdoz","slug":"frederic-berdoz","given":"Frédéric","family":"Berdoz","affiliation":""},{"name":"Dustin Brunner","slug":"dustin-brunner","given":"Dustin","family":"Brunner","affiliation":""},{"name":"Yann Vonlanthen","slug":"yann-vonlanthen","given":"Yann","family":"Vonlanthen","affiliation":""},{"name":"Roger Wattenhofer","slug":"roger-wattenhofer","given":"Roger","family":"Wattenhofer","affiliation":""}],"abstract":"Voting advice applications (VAAs) help millions of voters understand which political parties or candidates best align with their views. This paper explores the potential risks these applications pose to the democratic process when targeted by adversarial entities. In particular, we expose 11 manipulation strategies and measure their impact using data from Switzerland’s primary VAA, Smartvote, collected during the last two national elections. We find that altering application parameters, such as the matching method, can shift a party’s recommendation frequency by up to 105%. Cherry-picking questionnaire items can increase party recommendation frequency by over 261%, while subtle changes to parties’ or candidates’ responses can lead to a 248% increase. To address these vulnerabilities, we propose adversarial robustness properties VAAs should satisfy, introduce empirical metrics for assessing the resilience of various matching methods, and suggest possible avenues for research toward mitigating the effect of manipulation. Our framework is key to ensuring secure and reliable AI-based VAAs poised to emerge in the near future.","doi":"10.24963/ijcai.2025/1063","url":"https://ijcai.org/proceedings/2025/1063","pdf":"https://ijcai.org/proceedings/2025/1063.pdf","markdown":"https://ijcai.org/proceedings/2025/1063.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/1063","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"AI and Social Good","track":"AI and Social Good","number":1063,"pages":{"first":9564,"last":9572},"keywords":["Multidisciplinary Topics and Applications: General","AI Ethics, Trust, Fairness: General","Humans and AI: General"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}